Understanding Developers Privacy Concerns Through Reddit Thread Analysis

With the growing global emphasis on regulating the protection of personal information and increasing user expectation of the same, developing with privacy in mind is becoming ever more important. In this paper, we study the concerns, questions, and solutions developers discuss on Reddit forums to enhance our understanding of their perceptions and challenges while developing applications in the current privacy-focused world. We perform various forms of Natural Language Processing (NLP) on 437,317 threads from subreddits such as r/webdev, r/androiddev, and r/iOSProgramming to identify both common points of discussion and how these points change over time as new regulations are passed around the globe. Our results show that there are common trends in privacy topics among the different subreddits while the frequency of those topics differs between web and mobile applications

Towards Fine-Grained Localization of Privacy Behaviors

Mobile applications are required to give privacy notices to users when they collect or share personal information. Creating consistent and concise privacy notices can be a challenging task for developers. Previous work has attempted to help developers create privacy notices through a questionnaire or predefined templates. In this paper, we propose a novel approach and a framework, called PriGen, that extends these prior work. PriGen uses static analysis to identify Android applications' code segments that process sensitive information (i.e. permission-requiring code segments) and then leverages a Neural Machine Translation model to translate them into privacy captions. We present the initial evaluation of our translation task for ~300,000 code segments

Argumentation-based Methodology for Goal-oriented Requirements Language (GRL)

Goal-oriented Requirements Language (GRL) aims to capture goals and non-functional requirements of stakeholders and analyzing alternative solutions for realizing these goals. GRL also documents the rationale behind selecting certain goals or alternatives. However, it does not have any means to document and trace back all of the arguments that occur during the stakeholder’s discussion process. To address this, we have developed the RationalGRL framework. RationalGRL combines techniques for formal argumentation from artificial intelligence with goal modeling in GRL. However, we did not specify how practitioners can actually use this framework. In this paper we discuss the methodology for RationalGRL, which consists of two processes, goal modeling and argumentation, that can be done interchangeably. We motivate our approach with an example

RationalGRL: A Framework for Argumentation and Goal Modeling

Goal-oriented requirements modeling approaches aim to capture the intentions of the stakeholders involved in the development of an information system as goals and tasks. The process of constructing such goal models usually involves discussions between a requirements engineer and a group of stakeholders. Not all the arguments in such discussions can be captured as goals or tasks: e.g., the discussion whether to accept or reject a certain goal and the rationale for acceptance or rejection cannot be captured in goal models. In this paper, we apply techniques from computational argumentation to a goal modeling approach by using a coding analysis in which stakeholders discuss requirements for a Traffic Simulator. We combine a simplified version of a traditional goal model, the Goal-oriented Requirements Language (GRL), with ideas from argumentation on schemes for practical reasoning into a new framework (RationalGRL). RationalGRL provides a formal semantics and tool support to capture the discussions and outcomes of the argumentation process that leads to a goal model. We also define the RationalGRL development process to create a RationalGRL model

Rationalization of Goal Models in GRL using Formal Argumentation

Abstract—We apply an existing formal framework for practical reasoning with arguments and evidence to the Goal-oriented Requirements Language (GRL), which is part of the User Requirements Notation (URN). This formal framework serves as a rationalization for ele-ments in a GRL model: using attack relations between arguments we can automatically compute the accept-ability status of elements in a GRL model, based on the acceptability status of their underlying arguments and the evidence. We integrate the formal framework into the GRL metamodel and we set out a research to further develop this framework. Index Terms—User Requirements Notation, Goal-oriented Requirements Language, goal modeling, for-mal argumentation I